Computational Economics

, Volume 31, Issue 4, pp 381–395 | Cite as

A Pricing Mechanism for Resource Management in Grid Computing

  • Panos Parpas
  • Berç Rustem


We consider the problem of efficient resource allocation in a grid computing environment. Grid computing is an emerging paradigm that allows the sharing of a large number of a heterogeneous set of resources. We propose an auction mechanism for decentralized resource allocation. The problem is modeled as a multistage stochastic programming problem. Convergence of the auction allocations to the social optimum is established. Numerical experiments illustrate the efficacy of the method.


Grid computing Decentralized resource allocation Multistage stochastic programming 


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  1. Belenky, V. Z., Ivankov, S. A., & Volkonsky, V. A. (1976). On a certain approach to the investigation of the convergence of iterative processes. In Computing equilibria: How and why (Proc. Internat. Conf., Comput. Centre, Polish Acad. Sci., Toruń, 1974) (pp 225–247). North-Holland, Amsterdam.Google Scholar
  2. Birge J.R., Dempster, M.A.H. (1996). Stochastic programming approaches to stochastic scheduling. Journal of Global Optimization 9(3–4): 417–451CrossRefGoogle Scholar
  3. Birge J.R., Louveaux F. (1997). Introduction to stochastic programming. Springer Series in Operations Research. Springer-Verlag, New YorkGoogle Scholar
  4. Distributed European infrastructure supercomputing applications. Project information available online.
  5. Gnu linear programming kit, version 4.9, January 2006.
  6. Gupta A., Stahl D.O., Whinston A.B. (1997). A stochastic equilibrium model of internet pricing. Journal of Economic Dynamics & Control 21(4–5): 697–722CrossRefGoogle Scholar
  7. Holmberg K., Kiwiel K.C. (2006). Mean value cross decomposition for nonlinear convex problems. Optimization Methods & Software 21(3): 401–417CrossRefGoogle Scholar
  8. Papadimitriou, C. H. (2001). Algorithms, games, and the internet. In STOC.Google Scholar
  9. Rockafellar T., Wets R. (1991). Scenarios and policy aggregation in optimization under uncertainty. Mathematics of Operations Research 16(1): 119–147CrossRefGoogle Scholar
  10. Scarf H. (1967) The approximation of fixed points of a continuous mapping. SIAM Journal on Applied Mathematics 15: 1328–1343CrossRefGoogle Scholar
  11. Scarf, H. (1973). The computation of economic equilibria. New Haven, Conn: Yale University Press. With the collaboration of Terje Hansen, Cowles Foundation Monograph, No. 24.Google Scholar
  12. Stoenescu T.M., Teneketzis D. (2002). A pricing methodology for resource allocation and routing in integrated-services networks with quality of service requirements. Mathematical Methods of Operations Research 56(2):151–167CrossRefGoogle Scholar
  13. Sutherland I.E. (1968). A futures market in computer time. Communications of the ACM 11(6):449–451CrossRefGoogle Scholar
  14. The London e Science Centre. Project information available online.
  15. Thomas P., Teneketzis D., MacKie-Mason J.K. (2002). A market-based approach to optimal resource allocation in integrated-services connection-oriented networks. Operations Research 50(4):603–616CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  1. 1.Department of ComputingImperial CollegeLondonUK

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